Articles | Volume 18, issue 3
https://doi.org/10.5194/os-18-881-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-18-881-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data-assimilation-based parameter estimation of bathymetry and bottom friction coefficient to improve coastal accuracy in a global tide model
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Martin Verlaan
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Deltares, Delft, the Netherlands
Jelmer Veenstra
Deltares, Delft, the Netherlands
Hai Xiang Lin
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
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Jianbing Jin, Hai Xiang Lin, Arjo Segers, Yu Xie, and Arnold Heemink
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A Satellite Observational Operator (SOO) is proposed to translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The SOO makes the analysis step of assimilation comparable in the 3-D model space, and thus it avoids the artificial vertical correlations by not involving the integral operator in directly assimilating 2-D data. The results show that satellite data assimilation with SOO can efficiently improve the estimate of volcanic ash state and the forecast.
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Short summary
The accuracy of the Global Tide and Surge Model is significantly affected by some parameters. We correct the bathymetry and bottom friction coefficient with mathematical methods to improve model accuracy. The lack of tide gauge data in many coastal areas affects the correction process. We propose using observations from altimetry tidal products like FES2014 that have higher accuracy than our model to offset the data lack. Model accuracy is greatly improved, especially in the European shelf.
The accuracy of the Global Tide and Surge Model is significantly affected by some parameters. We...